A retrieval-augmented generation framework for synthesizing pedestrian–autonomous vehicle interaction research in virtual environments

The growing integration of autonomous vehicles (AVs) into urban traffic has intensified the need to understand how pedestrians perceive and respond to automated traffic environments. Virtual and extended reality (VR/XR) experiments have become essential tools for examining such interactions under safe and controlled conditions. However, different studies use different simulation platforms, experimental protocols, and analytical approaches, limiting cross-study comparability and cumulative understanding. This paper presents a retrieval-augmented generation (RAG) framework for conducting an AI-assisted systematic literature review of immersive pedestrian–AV interaction research. A structured Scopus query identified 52 empirical studies published in peer-reviewed journals. Each paper was processed through a multi-stage RAG pipeline that combined language-model-based extraction, semantic normalization, and network visualization. The framework harmonized heterogeneous terminology across studies and revealed methodological linkages among hardware, software, and analytical techniques. The adopted framework ensures transparency, scalability, and reproducibility in literature synthesis. Beyond summarizing current evidence on immersive pedestrian–AV research, this study demonstrates how retrieval-augmented workflows can advance reproducible, data-driven reviews in transportation and human-factors domains.